BackgroundTic disorder (TD) is a common chronic neuropsychiatric condition manifesting during childhood and adolescence. Jujuboside A (JuA) may alleviate TD symptoms; however, the mechanisms underlying its therapeutic effects remain unclear.MethodsWe established a rat model of TD and used histological techniques to evaluate the effects of JuA on pathological changes. We also measured 5-hydroxytryptamine (5-HT) and 5-hydroxyindoleacetic acid (5-HIAA) levels and assessed tryptophan hydroxylase 1 (TPH1) mRNA expression. Finally, we analyzed the gut microbiota composition in fecal samples using 16S rRNA metagenomic sequencing.ResultsJuA administration alleviated pathological changes in rats with TD, increased 5-HT and 5-HIAA levels, and upregulated TPH1 mRNA expression. Compared with no treatment, JuA treatment increased the proportion of Bacteroidia, Muribaculaceae, Bacteroidales, and Bacteroidota, while reducing that of Bacilli, Lactobacillaceae, Lactobacillus, Lactobacillales, and Firmicutes.ConclusionThese findings indicate that JuA mitigates TD progression, potentially by remodeling the gut microbiota and regulating 5-HT levels.
Neurocognitive function among individuals with problematic social media use
BackgroundWith the development of technology and the internet, social networks gained momentum quickly and play a central role in daily activities. Despite this, there is a public health concern over excessive or problematic social media use. There is also a debate whether excessive social media use should be considered as a behavioral addiction characterized by impulsivity or an impulse control disorder characterized by compulsivity. The goal of this study is to use neurocognitive tasks to investigate impulsivity and compulsivity among excessive social media users compared with non-excessive users.MethodThe study included 79 participants (age range 18 to 37), divided into two groups: 34 participants who excessively use social media (Mean Age = 23.03, SD = 2.71) and 45 participants who do not excessively use social media (Mean Age = 25.47, SD = 4.3). Participants filled out a demographic questionnaire, questionnaires on social media use, impulsivity, compulsivity, anxiety, and depression. They performed computerized cognitive tasks: GO/NO-GO (with Facebook and traffic sign pictures), Experimental Delay Discounting (EDT), and the Wisconsin Card Sorting Test (WCST).ResultsExcessive users of social media exhibited a lower ability to delay gratification on the EDT, indicating impulsivity. They made fewer non-perseverative errors on the WCST, which indicated high flexibility and test shifting, which is a contradicting evidence for compulsivity. Furthermore, on the GO/NO-GO task, individuals who excessively use social media made more omission errors in response to the “Facebook” sign compared to traffic signs (GO condition), indicating impaired selective attention. Finally, they also showed higher subjective ratings of anxiety, depression, impulsivity, and compulsivity.DiscussionThe results of this study provide evidence for impulsivity indicated by delay discounting tendency, which supports the behavioral addiction model, impaired selection attention and lack of evidence for compulsivity in excessive social media users. Further research on neurocognitive function in excessive social media users is required in order to determine whether it should be considered a behavioral addiction or an impulse control disorder.
Heatwave-related variations in psychiatric consultations and admissions: a time-series analysis
BackgroundHeatwaves are becoming increasingly frequent and intense across Europe, posing significant risks to physical and mental health. Emerging evidence suggests that prolonged exposure to high temperatures may exacerbate psychiatric symptoms and increase the demand for acute mental health services.ObjectivesThis study examined the relationship between extreme heat events and psychiatric service utilization in Bolzano, Italy, by analyzing emergency psychiatric consultations and acute psychiatric admissions across three non-consecutive years.MethodsA retrospective observational analysis was conducted using daily psychiatric consultations in the Emergency Department (ED) and daily admissions to acute psychiatric wards from 2013, 2018, and 2023. Meteorological data were obtained from the provincial environmental agency. Time-series analyses employed ARIMA models, incorporating daily minimum and maximum temperatures, tropical nights, and a cumulative heatwave index (n_hot_htwv). Model selection was based on BIC, and the effect of exogenous temperature variables was evaluated through changes in AIC. Residual diagnostics guided the inclusion of weekly seasonal dummy variables.ResultsNon-seasonal ARIMA models with day-of-week dummies provided the best fit for both consultations and admissions. Adding the cumulative heatwave variable (n_hot_htwv) consistently improved model fit across all years, whereas minimum and maximum temperatures alone did not. Heatwave duration emerged as a more sensitive predictor of psychiatric service utilization than isolated temperature peaks. No evidence of yearly seasonality was found, and residual diagnostics supported the robustness of models including weekly dummy variables.ConclusionHeatwaves are associated with increased psychiatric consultations and hospital admissions in Bolzano, with cumulative heat exposure representing a critical determinant. These effects cannot be explained solely by seasonal patterns, suggesting an independent climatic influence. Given the projected rise in heatwave intensity and duration, mental health services should incorporate climate-responsive planning and early-warning strategies.
Adding VR-Based Episodic Future Thinking Training to Executive- Function Training for Children With ADHD: A Randomized Controlled Trial
Conditions: ADHD
Interventions: Behavioral: executive function; Behavioral: a VR-based episodic future thinking
Sponsors: Qilu Hospital of Shandong University
Completed
Interventions: Behavioral: executive function; Behavioral: a VR-based episodic future thinking
Sponsors: Qilu Hospital of Shandong University
Completed
Comparative Outcomes of VR and In-person Interventions for Preschool-aged Children With Autism Spectrum Disorder
Conditions: Autism Spectrum Disorder High-Functioning; Autism Spectrum Disorder (ASD)
Interventions: Behavioral: Virtual Reality-Based Social Skills Group Intervention; Behavioral: In-Person Social Skills Group Intervention
Sponsors: National Cheng-Kung University Hospital
Completed
Interventions: Behavioral: Virtual Reality-Based Social Skills Group Intervention; Behavioral: In-Person Social Skills Group Intervention
Sponsors: National Cheng-Kung University Hospital
Completed
Personalized Pharmaco-Lifestyle Interventions for Severe Mental Illnesses (LIFETRAIN)
Conditions: Severe Mental Illness; Depression / Major Depressive Disorder; Bipolar Disorder (BD); Schizophrenia
Interventions: Drug: Semaglutide (SEMA); Behavioral: Exercise module; Behavioral: Anti-inflammatory diet module; Behavioral: Sleep intervention module; Behavioral: Social prescribing module; Device: Closed-loop transcranial alternating current stimulation (CL-tACS); Behavioral: Structured lifestyle psychoeducation; Device: Sham CL-tACS
Sponsors: Ludwig-Maximilians – University of Munich
Not yet recruiting
Interventions: Drug: Semaglutide (SEMA); Behavioral: Exercise module; Behavioral: Anti-inflammatory diet module; Behavioral: Sleep intervention module; Behavioral: Social prescribing module; Device: Closed-loop transcranial alternating current stimulation (CL-tACS); Behavioral: Structured lifestyle psychoeducation; Device: Sham CL-tACS
Sponsors: Ludwig-Maximilians – University of Munich
Not yet recruiting
Factors Influencing the Initiation and Continued Engagement of Digital Mental Health Tools Among Adults: Theory of Planned Behavior–Informed Systematic Review
Background: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding the shapes of initiation and ongoing use is essential for effective design and implementation. Objective: This study aims to synthesize determinants of adults’ initiation and engagement with DMHTs, organized through two lenses: (1) psychological factors aligned with the theory of planned behavior (TPB) and (2) design and access features. Methods: A systematic search of 9 databases (June 2025) identified qualitative and mixed methods primary studies reporting end-users’ experiences with DMHTs. Studies were screened and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quality appraisal used quality assessment with diverse studies (QuADS). Data were synthesized using a framework-guided thematic approach, mapping findings to TPB constructs and complementary design and access domains. Results: A total of 22 studies met inclusion criteria. Findings clustered into 2 interdependent domains. TPB constructs explained how beliefs, social expectations, and perceived control shaped decisions to start and persist with DMHTs. Design and access features frequently acted through these same pathways, especially by altering perceived behavioral control (PBC), with cost, connectivity, device constraints, and time flexibility affecting feasibility, with content design and privacy shaping perceived value and trust. Perceived fit (goals, cultural or linguistic relevance, and routine alignment) consistently influenced both initiation and continuation. Several features operated bidirectionally; depending on context, the same feature could facilitate or hinder engagement. Conclusions: Engagement with DMHTs is jointly determined by users’ beliefs and the design and access conditions within which tools are offered. Implementation should pursue a dual strategy, strengthening willingness to seek support (addressing attitudes, norms, and perceived control) while engineering low-effort, trustworthy, and context-appropriate experiences. Priorities include equity-focused policies (data costs, devices, and connectivity), transparent data practices, co-design with diverse communities, and consistent, theory-informed outcome measures.
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Reducing Intrusive Trauma Memories Using a Brief Mental Imagery Competing Task Intervention: Case Series of Trauma-Exposed Women in Iceland
Background: There is a need for scalable and simple interventions for trauma-exposed people. In this case series, we built on our previous case study and case series findings and further explored the use and potential effectiveness of a brief novel intervention to reduce the number of past intrusive memories of trauma. The imagery competing task intervention consists of a memory reminder and the visuospatial task Tetris played with mental rotation, targeting 1 intrusive memory at a time. Here, we test remote delivery of the intervention, including guidance from researchers without specialist mental health training, in a sample of women in Iceland with current intrusive memories from trauma. Objective: In a case series of trauma-exposed women, we aimed to explore whether this brief novel intervention reduces the number of established intrusive memories (primary outcome) and improves general functioning and symptom reduction in posttraumatic stress, depression, and anxiety (secondary outcomes). The acceptability of the intervention along with adaptations, that is, delivery by psychology students without specialist mental health training and digital delivery, was explored. Methods: Participants (N=8) monitored the number of intrusive memories from an index trauma (occurring 3‐16 years previously) in a daily diary at baseline, during the intervention, and postintervention at 1-month and 3-month follow-ups. The intervention was delivered digitally with guidance from clinical psychologists or psychology students. A repeated AB design was used (“A”: preintervention baseline, “B”: intervention phase). Intrusions were targeted one by one, creating repetitions of an AB design (ie, length of baseline “A” and intervention “B” varied for each memory). Results: The number of intrusive memories reduced for all participants from the baseline phase compared with the intervention phase, although the reduction was minimal for 2 participants (6.3%‐93%). The number of intrusive memories continued to reduce for 6 out of 8 participants (58%‐100% reduction at 1-month follow-up; 72%‐100% reduction at 3-month follow-up). Symptoms of posttraumatic stress, depression, and anxiety were reduced for most participants postintervention and continued to decrease during the follow-up periods. Functioning was improved for 7 of the 8 participants from baseline to postintervention and continued to improve at the follow-up assessments for 3 participants. The intervention delivered digitally and partly by students was perceived to be an acceptable way to reduce the frequency of intrusive memories by all participants (mean rating 9.5 out of 10). Conclusions: Data from this case series of traumatized women provide preliminary evidence for the effectiveness of this novel brief intervention in reducing intrusive memories of trauma occurring several years ago and in improving functioning and reducing core symptom burden. This study will inform a randomized controlled trial of this novel intervention, which may have considerable implications for large-scale clinical management of traumatized populations. Trial Registration: ClinicalTrials.gov NCT04209283; https://clinicaltrials.gov/study/NCT04209283 International Registered Report Identifier (IRRID): RR2-10.2196/29873
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Large Language Models and Their Applications in Mental Health: Scoping Review
Background: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. Objective: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. Methods: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. Results: In total, 41 papers were selected. Many studies included evaluations on OpenAI’s GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. Conclusions: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
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A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations
ObjectiveThis study aimed to develop and evaluate a two-layered machine learning framework that combines admission clinical information with longitudinal nursing observations to identify schizophrenia inpatients at high risk of self-harm or suicidal acts.MethodsWe retrospectively reviewed the records of 477 patients with schizophrenia hospitalised in Liaoning Province between July 2021 and July 2024. According to whether at least one self-injurious or suicidal episode was documented during the index admission, 159 individuals were assigned to a high-risk group and 318 to a non-high-risk group. At admission, 18 baseline variables (including age, sex, history of self-harm, hopelessness/depression, and educational attainment) were extracted from electronic medical records, and 39 nurse-rated behavioural items were scored weekly using the Psychiatric Patient Nursing Observation Scale. Static and dynamic feature sets were used to train six classifiers [regularized logistic regression (LR), support vector machine (SVM), extreme gradient boosting, random forest, multi-layer perceptron, and K-nearest neighbours]. The best static model (regularized LR) and the best dynamic model (SVM) were combined through probability-level weighted fusion to generate a hierarchical risk score.ResultsMultivariable analysis of admission features showed that previous self-harm [odds ratio (OR) = 4.323], hopelessness/depression (OR = 3.090), younger age (OR = 0.938), and higher educational level (OR = 1.357) were independent predictors of self-harm/suicidal behaviour. Among dynamic indicators, negative self-evaluation (OR = 2.303), self-reported depression (OR = 1.812), insomnia (OR = 1.768), talking to oneself (OR = 1.733), crying (OR = 1.700), and reduced conversation with others (OR = 1.422) remained significant. The optimised static LR model achieved an area under the curve (AUC) of 0.7564, and the dynamic SVM model reached an AUC of 0.8531. Their fusion further improved performance (AUC = 0.9048; sensitivity 0.8542; specificity 0.7789; accuracy 0.8042). This hierarchical model outperformed the best flat combined-feature model (SVM; AUC = 0.9022) in sensitivity (0.8542 vs. 0.6667), indicating a more clinically appropriate detection of high-risk patients.ConclusionA hierarchical machine learning approach that integrates baseline clinical history with repeated nursing assessments can effectively flag schizophrenia inpatients at high risk for self-harm and suicidal behaviour, supporting timely and individualised preventive strategies in psychiatric wards.